This study focuses on analyzing habitual basketball moves by developing an advanced basketball motion analysis system that utilizes deep learning techniques to accurately recognize and assess individual movement patterns in the sport. The system aims to help coaches and teammates understand players and each other more effectively, enabling quicker insights into movement patterns and team dynamics. The workflow begins with the recognition of single basketball actions. This stage employs MMAction2, enhanced with the MViTv2 model, to achieve high-precision identification of specific movements. This ensures accurate detection of fundamental basketball actions, providing a solid foundation for further analysis. In the next phase, the system uses a sliding windows technique to break down long video sequences into smaller, manageable segments. These segments are then analyzed using the MViTv2 model, allowing for the accurate identification of specific actions within extended basketball activities. Following this, the system adopts a two-step recognition process for more comprehensive action analysis. Initially, basketball movements are categorized into four broad action types. These categories are further subdivided into more granular movements to capture the nuances of each action. By combining insights from these stages, the system identifies users’ habitual basketball moves and provides a breakdown of movement patterns. To enhance usability, a sophisticated, user-friendly interface (UI) has been developed. This UI integrates facial recognition technology, enabling the creation of personalized databases for individual users. By automating user identification and data management, the interface streamlines interactions, making the system accessible and efficient. Overall, this basketball motion analysis system facilitates quicker understanding of movement patterns among players, coaches, and teammates. It promotes better communication and synergy within teams, ultimately supporting improved performance and coordination in basketball.